Multi-directional Multi-resolution Transforms for Zoom-Endoscopy Image Classification

  • Roland Kwitt
  • Andreas Uhl
Part of the Advances in Soft Computing book series (AINSC, volume 45)

Abstract

In this paper, we evaluate the discriminative power of image features, extracted from subbands of the Gabor Wavelet Transform and the Dual-Tree Complex Wavelet Transform for the classification of zoom-endoscopy images. Further, we incorporate color channel information into the classification process and show, that this leads to superior classification results, compared to luminance-channel based image processing.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kudo, S. et al. (1994) Colorectal tumorous and pit pattern. Journal of Clinical Pathology 47:880–885CrossRefGoogle Scholar
  2. 2.
    Konishi K., Kaneko K. et al. (2003) A comparison of magnifying and nonmagnifying colonoscopy for diagnosis of colorectal polyps. Gastrointestinal Endoscopy, 57:48–53CrossRefGoogle Scholar
  3. 3.
    Huristone, D.P. (2002) High-Resolution magnification chromoendoscopy: Common problems encountered in pit-pattern interpretation and correct classification of flat colorectal lesions. American Journal of Gastroenterology, 97:1069–1070Google Scholar
  4. 4.
    Meining, A. et al. (2004) Inter-and intra-observer variability of magnification chromoendoscopy for detecting specialized intestinal metaplasis at the gastroesophageal junction. Endoscopy, 36:160–164CrossRefGoogle Scholar
  5. 5.
    Hfner, M., Kendlbacher, C. et al. (2006) Pit Pattern Classification of Zoom-Endoscopic Colon Images Using Histogram Techniques. In: Proceedings of the 7th Nordic Signal Processing Symposium, 58–61Google Scholar
  6. 6.
    Hfner, M., Liedlgruber, M., et al. (2006) Pit Pattern Classification of Zoom-Endoscopic Colon Images Using Wavelet Texture Features. In: 3rd International Conference MEDSIP: Advances in Medical, Signal and Information Processing, pp. 37Google Scholar
  7. 7.
    Maroulis D. et al. (2003) CoLD: A versatile detection system for colorectal lesions in endoscopy video frames. In: Computer Methods and Programs in Biomedicine, 70: 151–186CrossRefGoogle Scholar
  8. 8.
    Karkanis, S.A., Maroulis D. et al. (2003) Computer-Aided Tumor Detection in Endoscopic Video Using Color Wavelet Features. IEEE Transactions on Information Technology in Biomedicine, 7(3):141–152CrossRefGoogle Scholar
  9. 9.
    Kingsbury N. (1998) The dual-tree complex wavelet transform: A new technique for shift-invariance and directional filters. In: Proceedings of the 8th IEEE DSP Workshop, pp. 9–12Google Scholar
  10. 10.
    Manjunath B.S., Ma. W.Y. (1996) Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8):837–842CrossRefGoogle Scholar
  11. 11.
    Rivaz P., Kingsbury, N. (1999) Complex wavelet features for fast texture image retrieval In: Proceedings of the IEEE Conference on Image Processing, pp. 109–113Google Scholar
  12. 12.
    Kittler, J. et al. (1998) On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3):226–239CrossRefGoogle Scholar
  13. 13.
    Selesnick, I.W., Baraniuk, R.G., Kingsbury, N. (2005) The Dual-Tree Complex Wavelet Transform. IEEE Signal Processing Magazine, 22(6):123–151CrossRefGoogle Scholar
  14. 14.
    Zuiderveld, K. (2004) Contrast Limited Adaptive Histogram Equalization. Graphics GEMS IV, pp. 474–484Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Roland Kwitt
    • 1
  • Andreas Uhl
    • 1
  1. 1.Department of Computer SciencesUniversity of SalzburgPoland

Personalised recommendations